ArticleInternational journal of women's health2026
The Landscape of Disulfidptosis in Preeclampsia Reveals a Novel 5-Gene Diagnostic Signature via Machine Learning.
Article in International journal of women's health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Preeclampsia (PE), a pregnancy-specific pathological condition, has shown a growing incidence over recent decades. Disulfidptosis is a newly discovered mode of programmed cell death that differs from traditional cell death pathways in its molecular mechanisms. Numerous studies have reported the association between disulfidptosis and various diseases; however, the role of disulfidptosis in the pathogenesis of PE remains unknown. Patients and Methods: This study first analyzed the expression patterns of disulfidptosis-related genes (DRGs) using the GSE75010 dataset. Based on these results, unsupervised consensus clustering was conducted specifically on the PE samples included in this dataset. Weighted gene co-expression network analysis and machine learning algorithms were utilized to identify hub genes related to PE and disulfidptosis clusters. Ultimately, the expression profiles of these hub genes were validated using the independent datasets GSE4707, GSE30186, and GSE54618, as well as quantitative PCR (qPCR). Results: 9 DRGs showed abnormal expressions in the PE samples ( Conclusion: This study proposes a new diagnostic model for PE, which can serve as a framework for studying disease heterogeneity and provides a basis for understanding the role of disulfidptosis in the occurrence of PE.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.